Precision Medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle. Autoimmune diseases are those in which the body’s natural defense system loses discriminating power between its own cells and foreign cells, causing the body to mistakenly attack healthy tissues. These conditions are very heterogeneous in their presentation and therefore difficult to diagnose and treat. Achieving precision medicine in autoimmune diseases has been challenging due to the complex etiologies of these conditions, involving an interplay between genetic, epigenetic, and environmental factors. However, recent technological and computational advances in molecular profiling have helped identify patient subtypes and molecular pathways which can be used to improve diagnostics and therapeutics. This review discusses the current understanding of the disease mechanisms, heterogeneity, and pathogenic autoantigens in autoimmune diseases gained from genomic and transcriptomic studies and highlights how these findings can be applied to better understand disease heterogeneity in the context of disease diagnostics and therapeutics.

Remote sensing survey in southern Jordan has identified at least three Roman temporary camps that indicate a probable undocumented military campaign into what is today Saudi Arabia, and which we conjecture is linked to the Roman annexation of the Nabataean kingdom in AD 106.

OBJECTIVES/GOALS: As hospitals across the nation respond to the need to address community violence, there is a dearth of Hospital-based Violence Intervention Programs (HVIPs) in the South despite having disproportionate rates. This research aims to identify key factors and strategies for implementation of an HVIP among rural patient populations in a southern state. METHODS/STUDY POPULATION: Semi-structured interviews will be conducted with medical providers, social service organizations, and patients transferred from four high-risk rural areas in Arkansas. Data will be analyzed using Framework Analysis, a rapid analysis approach involving framework development, code application, impactful statement identification, and content analysis. Evidence- Based Quality Improvement (EBQI), a group consensus making process, will be conducted to identify key implementation strategies and factors to adapt based interview findings. Priority areas for adaptation will be identified via systematic rating. The EBQI team, including researchers and key rural stakeholders will engage in a series of discussion, vote on final strategies, and develop a guide for future HVIP implementation and pilot testing. RESULTS/ANTICIPATED RESULTS: Findings from this study will result in a prioritized list of barriers and facilitators across sample groups. Factors will be rated by level of importance. Cluster maps will display the relationships among factors. Go and no-go zones will be identified based on importance and feasibility. Implementation strategies will be mapped to barriers and facilitators. DISCUSSION/SIGNIFICANCE: The findings will result in a culturally and geographically relevant HVIP model and package of implementation strategies to test in future hybrid trials (feasibility pilot & multi-site RCT); and shape the future of violence prevention efforts in healthcare settings across the rural South.

OBJECTIVES/GOALS: The rates of computational phenotyping algorithm reuse across health systems are low, leading to a proliferation of algorithms for the same trait. We propose a framework for reusing computational phenotyping algorithms and describe the real-world deployment of this framework for the development of the Colorado Diabetes EHR Research Repository. METHODS/STUDY POPULATION: The novel phenotype reuse framework consists of 4 steps: select algorithms that are appropriate for reuse by assessing whether they are fit for purpose; extend the algorithm to account for changes in data and care practice standards; localize the algorithm to use local database standards and terminologies; optimize the algorithm by applying a data driven approach to achieve the desired local performance. To identify individuals with type 1 diabetes (T1D) or type 2 diabetes (T2D), we selected and implemented T2D algorithms in a cohort of adults with any diabetes or pre-diabetes related diagnosis code, medication, or abnormal glucose-related laboratory test in the clinical data warehouse for UCHealth and the University of Colorado. RESULTS/ANTICIPATED RESULTS: We included a total of 926,290 patients who were identified by initial filters. Patients were more likely to be female (53%), identify as non-Hispanic white (69%) and had a median age of 58 years (IQR: 41, 70). Implementation, extension, localization, & optimization through iterative chart review prioritized high sensitivity for all-cause diabetes and high specificity for T1D and T2D. Of the original cohort, 252,946 (27%) were identified by the all-cause diabetes algorithm. Of these 11,688 were identified as T1D and 135,588 as T2D. After optimization the all-cause diabetes algorithm had 88% sensitivity, 90% specificity, 74% positive predictive value (PPV), and 96% negative predictive value (NPV). Our algorithms for T1D and T2D had high specificity (100% and 99%, respectively) and PPV (100 and 96% respectively). DISCUSSION/SIGNIFICANCE: Developing computational phenotyping algorithms is expensive and time consuming, yet algorithm reuse is low due to a lack of practical approaches for reusing algorithms. We demonstrate application of a novel framework for algorithm reuse, yielding good alignment of algorithm performance with study goals for identifying individuals with diabetes.